- Title
- On Least Squares Estimation When the Dependent Variable is Grouped
- Author(s)
- Mark B. Stewart Mark Stewart (University of Warwick and Princeton University)
- Abstract
- This paper examines the problem of estimating the parameters of an underlying linear model using data in which the dependent variable is only observed to fall in a certain interval on a continuous scale, its actual value remaining unobserved. A Least Squares algorithm for attaining the Maximum Likelihood estimator is described, the asymptotic bias of the OLS estimator derived for the normal regressors case and a "moment" estimator presented. A "two-step estimator" based on combining the two approaches is proposed and found to perform well in both an economic illustration and simulation experiments.
- Creation Date
- 1982-11
- Section URL ID
- IRS
- Paper Number
- 159
- URL
- https://dataspace.princeton.edu/bitstream/88435/dsp01j9602061c/1/159.pdf
- File Function
- Jel
- M49
- Keyword(s)
- Suppress
- false
- Series
- 1